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<p>This study explores the integration of artificial intelligence (AI) and finite element analysis (FEA) in spine surgery, highlighting their complementary roles across preoperative planning, intraoperative execution, and postoperative outcome prediction. The synergy between AI and FEA is reshaping modern spine care by improving biomechanical modeling, enhancing surgical precision, and enabling personalized treatment strategies. In the preoperative phase, AI-augmented FEA supports the design of patient-specific surgical plans, optimizing implant placement and simulating mechanical responses under various loading conditions. Intraoperatively, AI enables real-time image-guided navigation, robotic assistance, and automated anatomical recognition, reducing the risk of surgical error. Postoperatively, predictive models built on FEA simulations and patient data assist in tracking recovery, forecasting complications, and informing rehabilitation protocols. Together, these technologies contribute to a data-driven paradigm shift toward precision spine surgery. As intelligent feedback systems, digital twins, and autonomous surgical platforms continue to evolve, AI–FEA integration is poised to play a transformative role in delivering safer, more efficient, and individualized spine care.</p>

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Revolutionizing spine surgery with emerging AI–FEA integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月18日

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  • Christopher Franceschini,
  • Mohsen Ahmadi,
  • Xuanzong Zhang,
  • Kelly Wu,
  • Maohua Lin,
  • Ridge Weston,
  • Angela Rodio,
  • Yufei Tang,
  • Erik Engeberg,
  • Gui Pires,
  • Talha S. Cheema,
  • Frank D. Vrionis

摘要

This study explores the integration of artificial intelligence (AI) and finite element analysis (FEA) in spine surgery, highlighting their complementary roles across preoperative planning, intraoperative execution, and postoperative outcome prediction. The synergy between AI and FEA is reshaping modern spine care by improving biomechanical modeling, enhancing surgical precision, and enabling personalized treatment strategies. In the preoperative phase, AI-augmented FEA supports the design of patient-specific surgical plans, optimizing implant placement and simulating mechanical responses under various loading conditions. Intraoperatively, AI enables real-time image-guided navigation, robotic assistance, and automated anatomical recognition, reducing the risk of surgical error. Postoperatively, predictive models built on FEA simulations and patient data assist in tracking recovery, forecasting complications, and informing rehabilitation protocols. Together, these technologies contribute to a data-driven paradigm shift toward precision spine surgery. As intelligent feedback systems, digital twins, and autonomous surgical platforms continue to evolve, AI–FEA integration is poised to play a transformative role in delivering safer, more efficient, and individualized spine care.